Author Archives: Robert B. Tucker

About Robert B. Tucker

Robert B. Tucker is a globally recognized business futurist and president of The Innovation Resource Group in Santa Barbara, California. He has advised clients in 54 countries and authored eight books, including the bestsellers Managing the Future and Driving Growth Through Innovation. Tucker’s insights have guided organizations from IBM, Citibank, and American Express to the U.S. Army Corps of Engineers and the Dubai government. As one of the founders of the Innovation Movement, Robert has appeared on Bloomberg, Channel News Asia, Network 18 India, PBS, and was a featured guest on the CNBC series The Business of Innovation. A regular contributor to Forbes.com, Robert’s latest book is Build a Better Future: 7 Mindsets for Navigating the Age of Acceleration.

Use Failure as Rocket Fuel for Success Like SpaceX

Use Failure as Rocket Fuel for Success Like SpaceX

GUEST POST from Robert B. Tucker

SUMMARY: SpaceX’s early success, despite three rocket failures, exemplifies how embracing setbacks as learning opportunities drives innovation. Elon Musk fostered a culture of rapid “test, learn, redesign,” where organizational risks, not individual blame, fueled progress. This approach, contrasting with the common fear of mistakes, allowed SpaceX to overcome near collapse and achieve orbit. The article argues that true failure isn’t making errors, but failing to learn from them. Leaders must create environments encouraging prudent risk-taking, where post-mortems focus on lessons, not culprits. Adopting “fail fast and fail cheap” through small experiments helps organizations learn quickly, transforming setbacks into wisdom and better decisions for ultimate success.

Before SpaceX became one of the most valuable companies in the world, it suffered three consecutive rocket failures. By 2008, Elon Musk had invested nearly everything he had. The fourth launch wasn’t merely important — it was a matter of survival.

After Falcon 1 failed three consecutive times between 2006 and 2008, Musk did not conduct a witch hunt. Heads did not roll. Instead, he assembled his engineers, dissected the technical causes, and focused relentlessly on fixing problems and building morale before the next launch. The emphasis was always on emphasizing rapid learning and pushing ahead.

The successful fourth Falcon 1 launch took place on September 28, 2008. On that flight, Falcon 1 became the first privately developed liquid-fueled rocket to reach Earth’s orbit, a milestone many experts had considered nearly impossible for a startup company.

Had Falcon 1 failed a fourth time there might be no SpaceX today. Instead, that launch succeeded, NASA came calling, and a company that was weeks from collapse began its ascent toward bending history.

The lesson for leaders is profound: if you want people to innovate, you must create an environment where failure is an option, and where prudent risk-taking and rapid learning pervade your culture. SpaceX routinely tested rockets knowing they might explode because Musk believed real-world learning happened faster than endless analysis.

Early on, the fledgling start-up adopted a rapid “test, learn, redesign” cycle rather than trying to eliminate every possible risk before launch. Each unsuccessful launch produced engineering insights that were incorporated into the next design. In that sense, the first three launches were not really failures at all. They were expensive tuition payments on the road to success.

Take Away the Safety Net

Another of Elon Musk’s most important innovations wasn’t technological at all. It was organizational. In an industry long dominated by cost-plus contracts, where the federal government pays defense contractors for effort and expenses, plus a guaranteed margin of profit, regardless of results. Instead, Musk embraced milestone-based agreements with the government that essentially said, “Only pay us when we succeed.”

Taking away the safety net created enormous pressure on SpaceX. But it also unleashed extraordinary creativity and drive. Engineers were encouraged to think boldly, challenge “that’s the way we’ve always done it” thinking, and test ideas rapidly. The risks were borne by the organization, not by individual engineers. As a result, failure became rocket fuel rather than stigma.

One of the defining challenges facing young people today is an exaggerated fear of failure. Research shows that today’s students are significantly more anxious about making mistakes than previous generations. Many have come to believe that one wrong decision can derail a career, a reputation, or a future.

In today’s organizations, failure has become a taboo topic. We fear it. We hide it. We spend enormous amounts of energy trying to avoid it. Employees learn quickly which mistakes are acceptable and which ones can damage careers. As a result, people become cautious. They play defense instead of offense. They stop experimenting and growing in their careers. Obsolescence sets in.

Yet history tells us a different story. Almost every meaningful achievement — whether in business, innovation, politics, science, or personal growth — has been preceded by setbacks, disappointments, and outright failures.

Thomas Edison famously tested thousands of materials before finding a workable filament for his electric light bulb. When asked about his failures, he replied that he hadn’t failed at all. He had simply discovered thousands of ways that didn’t work.

Abraham Lincoln’s early career reads like a catalog of disappointments. He lost elections, suffered business failures, endured personal tragedies, and faced repeated public setbacks. Yet those experiences shaped the resilience and wisdom that ultimately carried him to the presidency during one of the most difficult periods in American history.

The lesson is not that failure is desirable. The lesson is that failure is often the price of admission for meaningful success.

The first step toward building a healthier attitude toward failure is being able to talk about them. I was fired from a dead-end corporate job early in my career and for years I hid my shame. Nowadays I realize I wasn’t fired but fired up! I realized that if I was ever going to become a self-supporting independent journalist, that I should seize that moment and dive in. I went on to become an expert in innovation, and a lucrative career that has taken me all over the world.

What I’ve found in teaching managers how to drive growth through innovation is that when mistakes are hidden, their value is lost. Others cannot learn from them. Valuable insights remain trapped inside individuals or departments. The organization pays the cost of the mistake but receives none of the educational benefit.

What I teach is that when there is a “failure,” that’s a good time to conduct a post-mortem after unsuccessful projects. Ask simple questions: What happened and why? What assumptions proved wrong? What can we learn? Most importantly, objective in-depth debriefs remove blame from the discussion. The goal is not to identify a culprit. The goal is to uncover lessons.

Organizations that openly discuss failures build institutional wisdom. Organizations that conceal failures repeat them.

True failure, therefore, is not making a mistake. True failure occurs when we fail to learn from mistakes — either our own or those of others.

Every industry is littered with examples of organizations that ignored warning signs that should have been visible to management. Kodak invented much of the technology behind digital photography yet failed to act on what it had learned. Blockbuster dismissed the significance of streaming. Nokia allowed a top down, risk adverse culture to congeal such that, when the iPhone hit the market, they were unable to pivot fast enough. Countless companies have repeated mistakes that competitors had already paid dearly to discover.

The most successful professionals cultivate the opposite habit. They become students of failure. They study what went wrong, why it went wrong, and how similar mistakes can be avoided in the future.

The risks associated with failure must be borne by the organization, not by individuals within the organization. When employees feel that every unsuccessful initiative could become a career-limiting event, innovation dies. Fear becomes the dominant operating system.

Leaders must create environments where people know that responsible experimentation is encouraged and protected. That does not mean tolerating carelessness or repeated mistakes. Accountability still matters. Preparation still matters. Execution still matters.

But when a well-conceived initiative fails despite thoughtful planning and diligent effort, the organization should absorb the risk and harvest the lessons.

People should not have to choose between innovation and job security.

This brings us to one of the most useful principles in modern business: fail fast and fail cheap.

Rather than investing years and millions of dollars pursuing untested assumptions, successful organizations run small experiments. They test ideas early. They gather feedback quickly. They adjust before costs escalate.

A small failure today can prevent a catastrophic failure tomorrow.

Think of it as buying information. Every experiment produces data. Some experiments confirm assumptions. Others disprove them. Both outcomes are valuable because they reduce uncertainty and improve future decisions.

The organizations that learn the fastest often outperform those with the greatest resources.

Ultimately, success is not achieved by avoiding failure. Success is achieved by creating systems that transform failure into learning, learning into wisdom, and wisdom into better decisions.

Edison understood this. Lincoln understood this. Musk understood this. Every accomplished entrepreneur, inventor, executive, and leader eventually learns the same lesson. Failure itself is rarely fatal. Refusing to learn from it often is.

The organizations that thrive in the future will not be those that make the fewest mistakes. They will be the ones that learn the fastest, adapt the quickest, and create cultures where intelligent risk-taking is not feared but encouraged.

After all, the opposite of failure is not success. The opposite of failure is learning.

This article originally appeared in Forbes

Image credit: Wikimedia Commons

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Why Students Are Booing Silicon Valley’s AI Vision

Why Students Are Booing Silicon Valley's AI Vision

GUEST POST from Robert B. Tucker

A curious thing happened at the University of Arizona’s commencement ceremony.

The speaker was former Google CEO Eric Schmidt, one of the most influential figures in the development of the digital economy. Addressing thousands of graduates, Schmidt spoke enthusiastically about artificial intelligence and the transformative role it will play in their lives and careers.

Then something unexpected happened. Students began to boo.

For many observers, the moment was jarring. Why would graduates reject a future of technological abundance, economic growth, and unprecedented innovation? Aren’t young people supposed to be technology’s biggest boosters?

Not anymore, apparently. As a futurist who has spent more than three decades advising leaders on adapting to change and innovation, I see this moment as an inflection point. I think what they were rejecting was a vision of the future being jammed down their throats. Looking at a bleak employment market, these young people were saying en masse, “Your vision of our future is not our vision of our future, and we don’t feel you really have our interest at heart.”

The question at this juncture is: What kind of future are we rushing headlong to build, and who will benefit?

The tech industrial complex spins an appealing vision. But it’s beginning to wear thin. Students and other segments of society are pushing back. They are asking tough questions: Will AI really solve humanity’s greatest challenges? Will it cure diseases, eliminate drudgery, unlock extraordinary productivity gains, and usher in a new era of prosperity, as the so-called tech visionaries proudly claim?

Or could it be that the underlying premise is faulty: that the more intelligence we can automate, the better off society will become. The young people are waking up to the possibility that this is hot air.

Across college campuses, among young professionals, and increasingly among the broader public, there is another narrative taking shape. It is one that many technology leaders seem to want to dismiss: growing unease about where all of this is headed.

Many Americans view AI through the lens of issues much closer to home: skyrocketing electricity bills caused in part by data center proliferation; teen chatbot addiction, and looming job displacement. A recent Stanford study, Canaries in the Coal Mine?, found that young workers in the most AI-exposed occupations saw a 16% relative decline in employment from late 2022 through September 2025.

Over the past several years, I have spoken with educators, business leaders, and students around the world. Increasingly, I hear variations of the emerging narrative. I hear people questioning the tech industry’s vision more sharply. Are we building tools that expand human potential, or tools that gradually replace us? The concern isn’t that AI will become more capable. The concern is that humans will become less so.

Scot Rabe has taught design at Ventura College for decades. He recently described his growing frustration with students. Attendance remains high, but engagement is declining. There is little evidence that students are wrestling deeply with ideas. In his words, “the lights are on, but nobody’s home.”

That observation aligns with broader concerns about what I call human agency—the capacity to act intentionally, make decisions, solve problems, and shape one’s own future.

A 2023 survey by the Pew Research Center explored the future of human agency in an increasingly digital world. Experts were deeply divided. Many predicted that emerging technologies would weaken individual autonomy rather than strengthen it.

Their concern deserves attention.

The challenge facing young people today is not simply learning how to use AI. It is learning how to remain fully human in a world increasingly designed to automate thinking, decision-making, and even creativity.

Tim Wu, author of The Age of Extraction, argues that many of today’s largest technology firms operate by extracting value from our attention, data, and behavior. The more time we spend scrolling, clicking, and consuming, the more profitable the system becomes.

But what happens when the same incentives are applied to intelligence itself? What happens when convenience becomes the highest value? What happens when every difficult task can be delegated to a machine? What happens to the development of judgment, wisdom, resilience, and imagination?

These are not anti-technology questions. They are profoundly human questions.

History suggests that societies thrive not when technology advances alone, but when human capability advances alongside it.

The printing press transformed civilization. Electricity transformed civilization. The internet transformed civilization. Yet none of these innovations eliminated the need for human initiative, purpose, or responsibility. If anything, they increased it.

The danger today is not that AI becomes more powerful. The danger is that we gradually surrender the very qualities that make us uniquely human. That may be what those students were trying to express.

Perhaps they were saying that they do not want a future in which every challenge is solved for them. Perhaps they do not want to become passive consumers of machine-generated answers. Perhaps they are pushing back against a worldview that sees efficiency as life’s highest goal.

And perhaps they are asking a deeper question: What role will humans play in the future being built around us?

One vision imagines a future that is increasingly automated, optimized, digitized, and controlled by a small number of powerful technology platforms. Another envisions a future where technology augments rather than replaces human capability. A future where innovation strengthens creativity, deepens relationships, expands opportunity, and reinforces human dignity.

The choice between these futures is being made right now. Every generation inherits a set of technologies. But every generation must also decide how those technologies will shape our lives.

The students who are booing Silicon Valley’s assumptions were doing more than expressing frustration at yet another out-of-touch billionaire. They were reminding us that progress is not simply about building smarter machines. Rather, it is about building a future worth inhabiting.

This article originally appeared in Forbes

Image credit: Wikimedia Commons

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My Advice for Today’s Graduates into This New World

My Advice for Today's Graduates into This New World

GUEST POST from Robert B. Tucker

A few years ago, I had the honor of delivering a commencement address at the University of California, Davis, my alma mater. Standing before thousands of graduates from nearly 100 academic programs and dozens of countries, I reflected on the extraordinary changes that had reshaped the world since my own graduation in 1978.

At the time, I believed the pace of change was accelerating. Now, I realize we were only at the beginning of a whole new age.

Today’s graduates face a world profoundly different from the one my generation entered. Artificial intelligence is reshaping entire industries in real time. Social media competes relentlessly for attention. Student debt burdens millions. Misinformation spreads faster than truth. Many young people feel anxious about jobs, housing, climate change, politics, and whether they will ever experience the stability previous generations often took for granted.

And yet, despite all this turbulence, I remain deeply optimistic about the future. As a futurist, I am also an historian. History tells us that every generation is handed its defining challenges. And every generation can rise above them with grit and intention.

The students graduating today possess tools, connectivity, access to knowledge, and opportunities that previous generations could scarcely imagine. But thriving in this era will require a new mindset. It will require the ability to navigate uncertainty without losing your humanity.

In my commencement address, I spoke about what I called the “Three C’s” of success: Change, Creativity, and Courage. I believe those three capacities matter now more than ever.

Embrace Change Without Losing Yourself

In 1440, Johannes Gutenberg invented the printing press. That invention disrupted the existing order and unleashed waves of transformation: the Scientific Revolution, the Enlightenment, the Industrial Revolution, and ultimately modern democracy itself.

Today, we are living through another revolution. But this one is happening exponentially faster.

Artificial intelligence, automation, biotechnology, robotics, and digital networks are transforming nearly every institution in society. Entire professions are being reinvented. Skills are becoming obsolete faster than ever before.

The challenge facing graduates today is not simply adapting to occasional disruption. It is learning to remain grounded while standing inside permanent acceleration.

Years ago, while backpacking in Wyoming’s Grand Teton Mountains, I wandered away from my campsite to watch a sunset. When I tried to return, an angry moose blocked my path. By the time the animal finally wandered off, darkness had fallen and I could no longer find my tent. I spent one of the coldest nights of my life huddled beneath a pine tree with only a forest service map as a blanket. At dawn, I looked around and discovered my tent was less than thirty feet away.

What I learned in the mountains that night was simple: conditions change rapidly when you’re not paying attention. That lesson applies powerfully today.

Many people resist change, deny it, or hope it will somehow go away. But the individuals and organizations that will flourish are those willing to keep their antennae up, pounce on opportunity, and be flexible.

That does not mean embracing every trend blindly. Some technologies and social movements deserve scrutiny, especially when they threaten human dignity, truth, freedom, or the common good. But the greatest danger is not change itself.

The greatest danger is drifting into passivity. Settling for comfort. Losing curiosity. Stopping your own growth. Congratulations on completing your education. But the future belongs to lifelong learners.

Cultivating Your Creativity Becomes Even More Valuable

A few years ago, IBM conducted a global study asking CEOs which leadership quality mattered most in an increasingly volatile and uncertain world. Their answer was creativity.

Not efficiency. Not technical expertise. Creativity.

That insight matters even more now.

Artificial intelligence can already summarize reports, generate marketing copy, write software code, and perform countless routine tasks faster than humans. But originality, imagination, emotional intelligence, judgment, and wisdom remain profoundly human capacities. The more the world automates average thinking, the more valuable original thinking becomes.

In 2006, I worked with a group of high-potential executives from Nokia, then the global leader in cell phones. During one session, I asked a simple question: “If I work for your company and I have an idea, what do you want me to do with it?”

One executive answered honestly. “I’d tell you to forget about it,” he said. “There’s so much bureaucracy you’ll never get anywhere with the idea.”

A year later, Apple introduced the iPhone and Nokia began its spectacular fall from grace.

In retrospect, Nokia believed it was in the cellphone business. Apple believed it was in the creativity business.

Going forward, we are all in the creativity business.

No matter what profession you enter, your future value will increasingly depend on your ability to connect ideas, solve problems, improvise, communicate, and create meaning in situations where no guidebook exists.

You are going to face moments where GPS is unavailable. Moments where there are few precedents. Moments where you must trust your instincts and make it up on the spot.

If you cultivate your creativity, you will not merely survive this era. You will be in demand.

Courage May Matter Most Of All

And that brings me to the third “C,” courage.

It takes courage to explore the frontiers of your field. It takes courage to face uncertainty without surrendering to fear. It takes courage to think independently when social pressure pushes toward conformity.

But in today’s world, courage increasingly means protecting your own mind.

With so many voices yammering at us from the moment we wake up until we close our eyes at night, it takes courage to decide what kind of life you truly want instead of letting algorithms, outrage cycles, or social media platforms decide for you.

It takes courage to focus deeply in an age of distraction.

It takes courage to disconnect long enough to think.

It takes courage to build something meaningful slowly while the world rewards instant reaction.

And above all, it takes courage to create the life you really want to live.

My generation came of age during Vietnam, Watergate, inflation, and enormous social unrest. Many people believed America’s best days were behind it. Yet innovation continued. Progress continued. New leaders emerged.

Now it is your generation’s turn at bat.

Do not let this age of acceleration reduce you to reacting, scrolling, comparing, consuming, and drifting. You were born to build, to create, to contribute, to love, and to lead.

Think big when others are thinking small. Push back against cynicism. Build a life, not just a resume.

The future is not something that simply happens to you. It is something you help create.

This article originally appeared in Forbes

Image credit: Pexels

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Age of Acceleration Will Be Transformational

Age of Acceleration Will Be Transformational

GUEST POST from Robert B. Tucker

A familiar narrative has congealed around artificial intelligence and the future: “AI is about to usher in an age of abundance. Machines will do the tedious work. Scientific discovery will accelerate. Medical breakthroughs will multiply. Productivity will soar.”

This is a compelling vision, and conference promoters are using it to put “butts in seats.” But it is also incomplete.

Focusing on the supposed benefits of AI ignores deeper questions:

What will this accelerated age really mean for work, human relationships, for trust, and for the underlying social fabric that allows our civilization to function?

Technologists have an economic incentive to sell AI’s bright vision. I’m convinced some of the finest evangelists on the planet reside in Silicon Valley. As a futurist, I have a duty to forecast the most likely future, without fear or favor, and to alert you to both threats and opportunities that lie ahead.

The “Age of Acceleration” Will Be Unlike Anything We’ve Ever Seen

Having researched these past six years what I call “MegaForces of Change,” I conclude that this new and accelerated age will be a wild ride: breakthroughs and breakdowns happening everywhere all at once. More change in the next 10 years than in the previous 100. Deep-seated and fundamental changes will compound and collide and challenge us as never before. The deeper impact of AI and other changes will not be measured merely in productivity gains or GDP growth. The consequence will be measured in how it reshapes the human experience itself.

Perhaps the least discussed effect of the speeded-up world will be decision overload. AI systems generate content, provide recommendations, point out options, and require our decisions at a scale far beyond anything we have previously encountered. The result is a psychological environment in which we must constantly be on guard in order to adapt, evaluate, and decide, at a pace faster than our cognitive wiring has evolved to handle.

The Questions Techno-optimists Chose to Ignore

There is another question rarely addressed in all the “AI will save the world” hype. If machines can replace human labor across a $50 trillion economy, what happens to everyone else? If machines can write articles, compose music, diagnose disease, and generate strategic plans, where does human uniqueness reside? And what about value creation? If the goal of AI is to replace human labor, what do people do in that world to earn money and find meaning?

Techno-optimists argue that humans will simply shift to more creative pursuits. They assure us that new jobs — and new job categories — will be created when old ones are disrupted: “Always have in the past, always will in the future.” But maybe not this time. In fact, creativity, judgment, storytelling, and design, domains once considered uniquely human, are precisely the areas where AI is advancing most rapidly.

Is the goal of technological civilization to optimize efficiency, or to preserve the richness of human experience? Travel writer Rick Steves has noted a growing trend of “de-staffing” in smaller European hotels. On a recent podcast, he noted that traditional, family-run establishments are replacing front-desk staff with automated check-in systems and digital keys. While this modernization may cut costs, Steves lamented that it often sacrifices the personal charm and local hospitality that define a classic, budget-friendly European travel experience.

Silicon Valley tech-sellers want to make everything a digital transaction, as if involving people is antiquated.

That question came up when a friend of mine was stranded for eight hours at the Dallas-Fort Worth airport. There was “not one human to talk to at the gate, and hundreds of people stranded without any human compassion, comfort, or accurate updates.” When robots or machines take over for humans, something serious and critical is being lost, noted Jennifer Freed in a recent Substack.

As daily life moves online, the incidental interactions that once built community (casual conversations, chance encounters, shared spaces, civic engagement) become rarer. Social isolation rises, and human flourishing becomes harder to achieve.

What Happens with Social Trust?

The decline in social trust is nothing new. A longitudinal study conducted by the University of Chicago shows a long-term decline in social trust in the United States dating back to the early 1970s. The core question asked by surveyors is whether “most people can be trusted” or whether “you can’t be too careful.” In the early 1970s, roughly 45–50% of Americans believed most people could be trusted. In recent years, that number has fallen into the low 30% range, sometimes lower depending on the survey year and subgroup analyzed.

Artificial intelligence seems likely to accelerate this decline even further. Deepfakes make it difficult to know whether a video is authentic. Misinformation and disinformation spew from politician’s social media at all hours, while cyber scams grow more sophisticated by the day. Identity theft, fraud, and online harassment have become routine features of the digital landscape.

Relationships In the Age of Algorithms

Another disquieting transformation is occurring in human relationships. Technology allows us to maintain contact with hundreds, or even thousands, of people. Yet these connections are often shallow and transitory. Social platforms reward visibility, speed, and engagement rather than depth or meaning. Communication becomes faster, thinner and blurred between authentic communication and autonomous.

At the same time, economic incentives increasingly shape digital relationships. Influencers, brand partnerships, subscription models, and algorithmic promotion blur the boundary between friendship and commerce. The result is a strange paradox. We are more networked than ever, yet genuine human connection is becoming rarer.

The Shrinking Attention Span

Communication itself is also evolving. Short-form video, algorithmic feeds, and constant notifications fragment attention into smaller slices. There’s little disagreement that our capacity for sustained undivided attention has sharply decreased in recent years. “By some measures you are lucky to get 47 seconds of focused attention on a discrete task, notes D. Graham Burnett, of the Friends of Attention Collective. “Deep reading, much less deep thinking, is next to impossible on that timeline, as are most forms of human interaction out of which meaningful life is made.”

Attention is not merely a mental habit; it is the foundation of reflection, empathy, and long-term thinking. When attention fragments, so does our ability to grapple with complex problems.

Civilization’s greatest achievements, from scientific discovery to democratic governance, require sustained attention and focus. Yet the digital ecosystem increasingly rewards the opposite.

The coming decade will test us in ways few people fully grasp today. It will challenge not only our industries and institutions, but our attention spans, relationships, sense of meaning, and ultimately our humanity itself.

The people who flourish in the years ahead will not necessarily be the most technologically sophisticated. They will be the most intentional, adaptable, grounded, and resilient. In a world increasingly shaped by intelligent machines, deeply human qualities like wisdom, empathy, creativity, judgment, and connection may become our greatest competitive advantage.

Technology will continue advancing at breathtaking speed. But whether humanity flourishes alongside it remains an open question.

The future will belong to those who prepare for it consciously, courageously, and with a clear sense of what it means to remain fully human.

This article originally appeared in Forbes

Image credit: Pexels

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Top Futurist Sees Major Healthcare Disruption Ahead

Top Futurist Sees Major Healthcare Disruption Ahead

GUEST POST from Robert B. Tucker

The future of healthcare is not coming, it is already here. And according to Delphi Group CEO Thomas Koulopoulos, it is unfolding far faster than most traditional providers are prepared to handle.

In a recent conversation in Harvard’s Science Center, Koulopoulos laid out a stark and compelling vision: healthcare is on the verge of being fundamentally restructured. It’s not being restructured by hospitals or insurers, but by technology, data, and a radical shift in who (or what) patients trust.

At the center of his prognosis is a simple but often overlooked truth about innovation. “We tend to focus on the product,” he explains. “The iPhone, the app, the device. But what actually drives innovation is process.” In healthcare, those processes are deeply entrenched, fragmented systems, outdated workflows, and institutional inertia that slow everything down. That is precisely why disruption is not only inevitable, but imminent.

Koulopoulos is not speaking theoretically. These days, he works across a handful of advisory and consulting roles, roughly seven or eight at any given time, many of them at the intersection of artificial intelligence and healthcare transformation. Nearly every engagement involves rethinking how care is delivered, not just improving it incrementally. The work is intensely process-centric, but the outcomes are tangible: new services, new delivery models, and entirely new ways of interacting with patients.

What has changed most dramatically, he notes, is healthcare’s willingness to look outside itself. A decade ago, a non-clinician advising healthcare systems would have been dismissed. Today, that openness reflects something deeper: a recognition that the biggest threat to healthcare is not internal inefficiency—it is external disruption.

“Amazon, Apple, Google, Meta, they all want to own your healthcare,” Koulopoulos says. “And in some ways, they are already doing a better job.”

That statement may sound provocative, but the evidence is increasingly hard to ignore. Patients are arriving at doctor visits armed with data from wearables, AI-generated analyses of lab results, and a level of insight that would have been unthinkable just a few years ago. In one recent example, Koulopoulos brought AI-driven health insights into a routine appointment. His physician was stunned—not just by the quality of the analysis, but by the depth of the conversation it enabled.

That interaction, he believes, is a preview of what comes next. Within a very short timeframe, measured in years, not decades, patients will increasingly turn to AI as their first point of consultation. Trust, particularly among younger generations, is already shifting in that direction. While older patients may hesitate, younger ones see not just the current limitations of AI, but its trajectory. They understand that what is imperfect today will improve rapidly—and they are willing to bet on that curve.

But the real disruption goes far beyond diagnostics. It strikes at the core structural weakness of healthcare systems worldwide: the absence of continuity.

“Ask yourself a simple question,” Koulopoulos says. “Could you pull together your entire medical history from the past ten years in five minutes?” For most people, the answer is no. Records are scattered across providers, insurers, pharmacies, and systems that rarely communicate with one another. The result is inefficiency at best—and dangerous fragmentation at worst.

The solution he envisions is what he calls the “digital advocate,” a personal, AI-powered twin that holds a complete, longitudinal record of your health. This is not a distant concept. Koulopoulos already maintains such a system for himself, integrating years of medical data into a single, accessible interface that allows him to analyze trends, question anomalies, and make informed decisions in real time.

In this model, the patient (not the provider) becomes the central node in the healthcare ecosystem. The digital advocate does not operate in silos; it integrates everything, from lab results to imaging to behavioral patterns. It can speak for you, guide you, and coordinate your care. In aging populations, it may even serve as a surrogate voice when patients can no longer advocate for themselves.

This shift has profound implications. It effectively leapfrogs the existing system rather than attempting to fix it incrementally. And it introduces a new kind of intelligence into healthcare—one that is continuous, personalized, and deeply contextual.

The healthcare system is already in shock

At the same time, Koulopoulos and other healthcare futurists point to another overlooked force of disruption: the tortured economics of healthcare, and globally, not just in the US. The entire system is in cardiac arrest.

“Hospitals have essentially never recovered from the multiple shocks of COVID,” observes futurist Langdon Morris. “The entire delivery chain was so massively disrupted that it has not recovered. And the non-recovery threatens to bankrupt many hospitals, which would in many cases be disastrous for the communities they serve.” Disruptive companies are coming fast for all the incumbents in all markets, and the future winners in many markets will be the ones who do the best job of integration. “The days of standalone technology are finished,” Morris believes. We expect a major disruption in the supplier ecosystem.

The demand for healthcare in the future will diminish, according to forecasters. Advances in treatment may turn diseases like cancer into manageable chronic conditions rather than acute crises. Autonomous vehicles, longer term, could dramatically reduce accident-related injuries, a major source of emergency room visits. Each of these shifts erodes the volume-based economics that underpin much of today’s healthcare infrastructure.

The result? “Traditional providers are going to be in a world of pain,” Koulopoulos says bluntly.

Yet the greatest obstacle to transformation may not be technological, it is cultural. Organizations cling to what has worked in the past, even as the ground shifts beneath them. The pattern is familiar. Kodak protected film. Blockbuster protected brick-and-mortar stores. Healthcare providers risk protecting legacy systems at the expense of future relevance.

So what should healthcare leaders do?

In our interview, Koulopoulos was clear: start by building internal capability to understand and apply these technologies. That means not just experimenting with AI, but actively integrating it into clinical workflows—everything from “ambient listening” during patient visits to AI-assisted communication that translates complex diagnoses into understandable, actionable language.

Equally important is embracing the broader ecosystem. Patients are already using wearables, at-home diagnostics, and digital tools that operate outside traditional systems. Providers can either ignore these inputs, or integrate them into a more holistic model of care.

The competitive threat is not theoretical. In one case, Koulopoulos compared a hospital-based sleep study, typically requiring weeks of waiting and multiple appointments, to an at-home diagnostic device that delivered equivalent results in 72 hours. The question he posed to the provider was simple: how do you compete with that?

There was no easy answer.

Ultimately, the future of healthcare will not be defined by any single breakthrough, but by the convergence of technologies, processes, and shifting expectations. Patients will navigate an ecosystem of options, choosing convenience and speed alongside quality. Providers who resist this shift will find themselves increasingly marginalized.

Those who adapt, however, have an opportunity to redefine their role, not as gatekeepers of care, but as orchestrators within a dynamic, patient-centered system.

Koulopoulos sees that future clearly. The question is whether the rest of the industry is willing to see it, and act, before it is too late.

This article originally appeared in Forbes

Image credit: The Delphi Group

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Has the Innovation Movement Hit a Wall?

Has the Innovation Movement Hit a Wall?

GUEST POST from Robert B. Tucker

For three decades, I’ve had a front-row seat observing the global Innovation Movement. Before chief innovation officers, before design thinking workshops, before Harvard Business School professor Clayton Christensen rocked our world with The Innovator’s Dilemma, I was writing about thinkers, innovators, and visionaries and the future of innovation.

In the late 1990s and early 2000s, a powerful realization swept through the corporate world: Mergers and acquisitions were no longer enough. Operational excellence was not enough. Organic growth was paramount; new sources of value needed to be discovered. New business models imagined. Companies across the board realized that innovation was no longer optional. It was becoming central to long-term survival. A movement towards unleashing innovation was born.

Vanguard companies like Procter & Gamble, IBM, Citibank, Whirlpool, and others led the way. Part discipline, part crusade, the Innovation Movement was grounded in the notion that innovation was a strategic discipline, not a side issue.

Over the next 25 years, a host of new tools, metrics, and frameworks were invented to help firms get better at driving growth through innovation. As a futurist and innovation champion, I helped spread the gospel of “innovation as a permanent corporate practice” from Mumbai to St. Petersburg, and from Silicon Valley to Istanbul and China.

Those were heady days, but fast forward to today. Sure, the language of innovation remains. But something has shifted. In conversations with executives, I increasingly hear a quiet question: Has the Innovation Movement stalled out?

Over breakfast at Boston’s storied Charles Hotel, I explored this possibility with Scott Kirsner, co-founder of InnoLead, a 1500-organization consortium of innovation practitioners, and a keen observer of the Movement since its inception. Scott’s take was blunt.

The AI Meteor

“The innovation movement is in a tough place,” he told me. “It feels like we’re wandering in the woods. Or like a meteor just hit.” The meteor, in his view, is AI.

When meteors strike, they expose those who can adapt and those who cannot or will not. Scott agreed that in many organizations today, innovation had lost its luster. It was embraced but never really embedded. More than a few early converts allowed it to become a department, a lab, a flavor of the month. It was funded in good times and quietly cut when pressures mounted.

Kirsner, just returned from a snowboarding trip in New Hampshire, described a familiar pattern: companies launch innovation teams, shut them down after a few years, and then restart them later. For many, innovation returned to being on-again/off-again, treated as a project rather than a capability.

Even before generative artificial intelligence hit like a meteor, the Movement began its fall from grace. It did not decline dramatically; it dissipated slowly over time. In some firms, it became performative—more about buzzwords than disciplined analysis of customer pain points and experimentation.

In recent years, innovation has become a meaningless buzzword, relegated to marketing hype. Interest in breakthrough new products, services, and business models has evaporated. These days, cost-cutting, risk management, and next quarter’s earnings dominate decision-making; risk-aversion is the new mantra.

The word innovation was co-opted by the bean counters and the marketers. In short, innovation has become a cover for profit extraction, rather than new value creation that benefits the customer. That was the central guiding principle of the Innovation Movement: you innovate to create new customer value. And you thereby capture some of that value in the form of profits for the effort.

Many executives don’t even use the word innovation anymore. They prefer to talk about growth. As Kirsner sees it, growth is harder to argue against. But this is largely a semantic change. Innovation was always a means to growth, as I described in ‘Driving Growth Through Innovation’ in 2001, through new products, services, partnerships, R&D discoveries, and new markets.

Another narrative has emerged that is now working against the Innovation Movement: that, as hard as they try, large companies cannot innovate internally. So why not save yourself the trouble and simply acquire startups?

This is a seductive argument—but one with flaws. The strongest companies do both, argues Kirsner. Google built Google Video before acquiring YouTube. The internal effort failed commercially but provided insight. Disney built its cruise business from scratch rather than buying into the market. These cases remind us that internal innovation is not obsolete. But it does require patience—something many organizations lack.

Persistent Innovators

Kirsner refers to the long-term devotees as “persistent innovators,” companies like Nike, LEGO, Novartis, and Google, where innovation is baked in their DNA, part of how they operate. In my own practice, I have long distinguished between the Discovery Engine and the Delivery Engine. Most companies are proficient at delivery, execution, efficiency, and scale. Far fewer truly understand or invest equally in discovery—scanning and monitoring the external and industry environments, experimenting, disrupting, and creating new markets for future growth. The companies that endure do both.

For all the hype, AI may be a helpful tool to innovators with the right mindset. Most important is what it removes: friction. One of the biggest barriers to innovation has always been the cost and time required to experiment. AI collapses that.

“If you have five ideas,” Kirsner noted, “you can [now] prototype all five.” Instead of building endless slide decks, teams can create tangible representations—a short video, a working model, a simulated experience. “The shift from telling to showing [that AI enables] is profound. It lowers the cost of learning.”

At the same time, AI may widen the gap between startups and large organizations. Startups adopt tools instantly. They experiment without permission. Large companies remain slowed by procurement cycles and internal constraints. By the time a tool is approved, it may already be outdated. The advantage is shifting to those who can act fastest, and to those with a well-oiled innovation process in place.

There is also the question of whether AI can truly innovate. Trained on the past, can it produce something genuinely new? Kirsner’s answer is pragmatic. Most innovation is combinatorial: recombining existing elements in new ways. The iPhone was not invented from scratch; it integrated existing technologies into something transformative. AI can assist in generating these combinations. But the human role remains central: framing problems, judging outputs, and deciding what matters are all areas where AI struggles.

So, where does this leave the Innovation Movement? Not dead certainly, but at an inflection point. Its first era was about persuasion, convincing leaders that innovation mattered. The next era will be about capability, embedding innovation into how organizations actually build a better future.

The meteor has struck. As Scott Kirsner sees it, what emerges from the dust will depend on who adapts, and who does not.

This article originally appeared in Forbes

Image credit: Pexels

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Will New Jobs Replace Those AI Wipes Out?

Will New Jobs Replace Those AI Wipes Out?

GUEST POST from Robert B. Tucker

For years, economists and technologists have comforted the public with a familiar refrain: as new technologies destroyed jobs, new ones arose even faster. The tractor displaced farm laborers, yet factories absorbed them. Computers replaced typewriters but created programmers.

The pattern seemed reassuringly predictable. Creative destruction, we were assured, always has a job-producing rainbow at the end of the storm. But artificial intelligence is not simply another tool like the computer or the tractor.

For starters, AI doesn’t just augment human capability in a narrow domain. It is a multi-faceted system that learns, adapts, writes, designs, diagnoses, analyzes, composes, and increasingly decides. In other words, AI is not replacing a single category of work. Rather, it is encroaching simultaneously on dozens. White-collar, creative, analytical, and technical roles are all within its expanding reach.

The first loud alarm bell of mass job displacement came in 2025, when Anthropic CEO Dario Amodei warned in an Axios interview that AI could eliminate “roughly 50% of entry-level white-collar jobs within 1–5 years, and that unemployment could spike to 10–20% within one to five years.”

To be sure, new jobs are appearing. According to LinkedIn’s Economic Graph—the world’s largest real-time map of jobs and skills, over 1.3 million AI-related job opportunities have appeared in the past two years alone. Many of these jobs did not even exist five years ago. But many of these jobs are specialized, technical, or niche. Meanwhile, large-scale occupations employing millions are shrinking.

“Something big is happening,” noted AI investor and CEO Matt Shumer, in an influential post in February 2026, read by 80 million people. “I am no longer needed for the actual technical work of my job. I describe what I want to be built, in plain English, and it just appears. Not a rough draft I need to fix. The finished thing. I tell the AI what I want, walk away from my computer for four hours, and come back to find the work done better than I would have done it myself.”

Citrini Research added to the with a new strain of fears about AI, painting what The Wall Street Journal called a “dark portrait of a future in which technological change inspires a race to the bottom in white-collar knowledge work. “For the entirety of modern economic history, human intelligence has been the scarce input,” Citrini noted. “We are now experiencing the unwind of that premium.” The Dow dropped 820 points on the post.

As AI models are becoming capable of building AI models, the pace of progress in AI has become exponential rather than linear. As the implications of recent advances cascade throughout the economy, stock markets gyrate, and career anxiety pervades the white-collar sector.

This new reality should prompt us to question the breezy optimism that “new jobs will appear.” Of course they will. The real question is: what kind of jobs?

Gig economy jobs have exploded over the past two decades. In 2005, only about 10% of the U.S. workforce participated in gig or independent work. Today that share has surged to roughly 35–38% of workers—about 60–70 million Americans—and still growing. In one sense, gig work offers freedom: flexibility, autonomy, and the ability to diversify income streams. For many workers it’s a hedge against layoffs and economic volatility. But the downsides are equally real. Gig workers often lack employer benefits, job security, retirement plans, and predictable income—and many earn less per hour than in traditional roles.

Yet another occupation often cited as evidence of this “new jobs will appear” optimism is the rise of the social media influencer. In theory, it represents a new category of work born of the digital economy—individuals building audiences, shaping tastes, and monetizing attention. Some sources have suggested that those who manage to accumulate over 50,000 followers could pull in an income of between $40,000 and $100,000 a year.

But the reality of this new job category, at least for some, hides a darker reality. Wellness influencer Lee Tilghman built a large Instagram following and earned hundreds of thousands from brand-sponsored posts. Yet behind the bright lights, she battled anxiety, loneliness, and disordered eating while spending up to ten hours a day online chasing validation. The constant pressure to post content became a ball and chain, which Tilghman later called “performing your life for content.” Suffering from stress and the recurrence of an eating disorder, she quit and now works a traditional 9-5 job which stops at the end of the day. As she told The New York Times, “When you’re an influencer, then you have chains on.”

A growing share of our economy may be shifting from producing tangible value to competing for attention inside algorithm-driven platforms. Millions of aspiring influencers chase likes, followers, and brand partnerships, yet only a tiny fraction earn a stable living. The rest exist in a precarious ecosystem of constant posting, self-promotion, and digital performance. “The information economy that we are currently building is really a new form of feudalism,” notes technologist Jaron Lanier.

In other words, the “new jobs” created by the technological revolution is often not a profession at all; it is a lottery. And even here, AI is moving rapidly. Synthetic influencers, automated content creation, and algorithmically generated personalities are already beginning to crowd the space.

The deeper issue is not simply employment but meaning. A society in which vast numbers of people struggle to find work that is steady, economically viable, socially valued, and personally fulfilling will face pressures far beyond the labor market.

In the Age of Acceleration, the question is no longer whether technology creates or destroys jobs. The question is how fast we adapt.

This article originally appeared in Forbes

Image credit: Pexels

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Leaders Must Think Like Futurists as Change Accelerates

Leaders Must Think Like Futurists as Change Accelerates

GUEST POST from Robert B. Tucker

With war in Iran disrupting the global energy supply, AI breakthroughs threatening jobs, and political, demographic, and social forces colliding everywhere, we may be entering an age where disruption is the permanent new normal.

The question is what to do about it.

In this new age of constant acceleration, traditional approaches to planning are out the window. Leaders who rely on methods that worked in the past may find themselves reacting to events, forever firefighting, and playing catch-up. In an age defined by compounding disruptions, leaders cannot rely solely on incremental planning, quarterly metrics, or yesterday’s assumptions.

What this moment demands is a different discipline altogether: the ability to think like futurists and visionaries and reinvent how we navigate the future.

That was the central message delivered recently by leading futurist Rachel Hatch of the Institute for the Future, at the 2026 REACH Ideas and Action Summit in California’s Central Coast region. “The future is far too complex and uncertain and combinatorial to predict,” Hatch told the gathering of industry, government, and nonprofit leaders at the UC Santa Barbara campus.

“If someone’s pretending that they can predict the future, they probably shouldn’t be trusted. But what we can do is to help people think more systematically and creatively about how the future might be different. And adopt certain foresight tools to guide us forward.”

The Institute was founded in 1968 to study long-term societal and technological change. The Palo Alto, California, nonprofit conducts foresight research that helps businesses, governments, and nonprofits to map scenarios and better prepare for change.

Hatch and her fellow researchers collaborate with organizations as varied as the Episcopal Church, public transit agencies, universities, nonprofits, and economic development consortia such as REACH, which organized the one-day conference on the UCSB campus.

In times like these, noted Hatch, leaders must do two things at once: improvisation and imagination. “Most of you are already improvising all day long, responding to surprises, disruptions, funding shifts, political whiplash, labor shortages, new technologies, and changing expectations. But tapping your imagination, and opening the mental space to think deeply and systematically about what might come next, is often crowded out by urgency.”

To Hatch, this reactive leadership style is a mistake. In such environments, preparedness becomes a core leadership discipline. “Preparedness does not mean predicting the future with precision. It means developing the habits, mindsets, and frameworks that allow us to respond wisely to multiple plausible futures before they arrive at full force.”

Hatch made this point memorably by recalling an exercise the Institute conducted in 2008, led by IFTF colleague Jane McGonigal. Using a game called Superstruct, one of the scenarios the group explored was the possibility of an out-of-control global respiratory pandemic. Participants were invited to imagine a scenario where a virus went rogue and ponder, “If this were to happen, what would you do? How would you respond?”

One participant in the 2008 exercise mentioned digging old paint masks out of the garage. Another imagined supply chains breaking down. A third wondered how parents would work while simultaneously schooling their children at home. The value of the exercise, Hatch noted, was not that people would someday declare themselves “fully prepared to face a pandemic,” but that they developed what she called “a little bit more of a readiness posture” by allowing themselves to imagine a future they would rather not contemplate.

That phrase—readiness posture—is exactly right for today’s mass uncertainty and acceleration. And it encapsulates the value of futurist thinking. We cannot know with pinpoint accuracy which event will happen on which date. After all, the future is, as Hatch put it, “far too complex and uncertain and combinatorial” for that.

But we can think more systematically and creatively about how the future might be different, and we can use that thinking to make better choices now. Which is where the futurist tool of strategic foresight comes in.

Hatch defined foresight as “a set of tools, processes, and mindsets for developing strategy and making decisions under conditions of uncertainty.” Techniques can be taught. Tools can be adopted. Yet the hardest part is shifting our thinking and letting in different points of view. “Helping leaders loosen their grip on the ‘official future’ that they have been assuming will happen— takes humility, curiosity, and courage. And often a new mindset.”

Hatch identified several cognitive traps that make future-focused thinking difficult. One is the bias toward precision metrics: the belief that the more data we gather, the more certainty we possess. Data matters, of course. But when leaders become intoxicated by dashboards and forecasts, they often mistake numerical precision for strategic insight. The result is overconfidence, followed by disruption.

Another trap is what she calls “official futures.” Every organization has one: a default set of assumptions about what the market will do, how customers will behave, how technology will unfold, or what success will look like three years from now. These assumptions can create alignment, but they can also become blinders. As Hatch reminded her audience, Nokia once looked unassailable. The official future said so—right until it didn’t.

Then there is the deeply human issue of anxiety about change. Fear narrows imagination. Stress locks us into defensive postures. When people feel under threat, they want to protect the budget, the institution, the business model, the identity, the orthodoxy. But that instinct, however understandable, can make it harder to see emerging possibilities. In my language, this is why adaptability and anticipatory thinking must be cultivated before disruption peaks, not afterward.

Perhaps Hatch’s most fascinating insight came from neuroscience. Research suggests that when people think about their future selves, the brain often responds much as it does when thinking about a stranger. In other words, the future self can feel abstract, distant, and only weakly connected to the present self. That helps explain why leaders, institutions, and even regions so often underinvest in long-term resilience. The future feels real intellectually, but not viscerally.

So how do leaders overcome these traps? Hatch’s answer is both practical and powerful: collect signals of change. These are “vivid, surprising, specific observations about how the world is changing today.” They are not vague trend statements. They are concrete clues—new behaviors, strange business models, emerging technologies, shifting values—that reveal how the future is already arriving in uneven ways.

Futurist and science fiction writer William Gibson famously said, “The future is already here. It’s just not evenly distributed.” Hatch rightly brought that line into her lecture, and it speaks to one of the most under-appreciated disciplines in leadership: learning to spot the future early, while it is still scattered, local, and easy to dismiss.

Hatch’s examples were appropriately provocative. One involved a startup called Rent-a-Human, where AI agents deploy humans into the physical world to perform tasks they themselves cannot do. Another focused on the growing scale of prediction markets and what Hatch called the possible “gamblification economy,” in which younger generations (disillusioned about conventional paths to prosperity) turn increasingly to betting, speculation, and crypto as alternative financial strategies. These examples may sound fringe, even absurd. But as the pioneer of modern futures thinking Jim Dator observed, any useful statement about the future should sound ridiculous at first.

As a fellow futurist, what I especially appreciated was Hatch’s insistence that foresight must lead to action. “Foresight should never be about bright, shiny futures,” she said. “It’s not about naval gazing.” The point is not to marvel at novelty. The point is to make better decisions, allocate resources more wisely, and build stronger institutions while there is still time to do so.

When the pace of change was slower, leaders could get by with experience, instinct, and incremental adjustment. That era is over. In the Age of Acceleration, the advantage will go to those who can widen the time horizon, detect signals early, challenge the official future, and build what Rachel Hatch aptly calls a readiness posture.

Thinking like a futurist is no longer a niche exercise for specialists; it is becoming the defining leadership competency of our time.

This article originally appeared in Forbes

Image credit: Pexels

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Why Building Trust Matters in the Age of Acceleration

Why Building Trust Matters in the Age of Acceleration

GUEST POST from Robert B. Tucker

The recent release of the Jeffrey Epstein files, revealing the involvement of numerous high-profile figures, has laid bare the diminution of trust in modern society —and the urgent need to reverse the slide.

Public reaction to episodes involving powerful insiders, whether in the corporate world, reveals causation in the downward slide. Trust erodes when people suspect the rules are not applied evenly. When powerful systems protect insiders, while ordinary standards apply to everyone else, the result is cynicism and distrust.

The warning lights have been flashing for decades. And now, at a time when artificial intelligence is working its way into all realms of life, and when information and misinformation travel instantly around the globe, and when the speed of change is increasingly exponential, the temptation is to retreat into suspicion and tribalism.

Trust was once the glue that bonded relationships and societies together. Honesty and truthfulness were the operating system that enabled strangers to cooperate, institutions to function, businesses to make deals, and countries and communities to build better futures.

But trust cannot be assumed in today’s world. It must be earned, created, and guarded.

The collapse of trust started decades ago. Surveys from Pew, Gallup, and from social-capital research stretching back to the 1970s all tell a similar story: confidence in institutions, leaders, media, business, and even neighbors has been on the decline for decades.

Harvard sociologist Robert Putnam was among the first to reveal the social dimension of this disintegration in his landmark book, Bowling Alone: The Collapse and Revival of American Community. His research found that civic engagement and community participation peaked in the late 1960s, before steadily declining thereafter. Americans stopped joining clubs and attending church. Neighborhood interaction declined. Shared civic rituals began to fade.

The result has been the slow erosion of social capital – the invisible glue that makes cooperation possible.

The University of Chicago’s General Social Survey is one of America’s longest-running social studies. In 1972, when the study began, nearly half of Americans believed “most people can be trusted.” By 2018, that number had fallen to 33%. In the 2024 survey, trust between fellow human beings had fallen to 25%.

The gold standard of trust measurement is the annual Edelman Trust Barometer. For 25 years, Edelman has tracked confidence in four institutions: government, media, NGOs, and business. Created in response to globalization protests and widening skepticism toward elites, the survey now spans roughly 30 countries and tens of thousands of respondents annually, offering a rare multi-decade, multi-cultural window into the psychological state of trust.

Recent findings show a widening “trust gap” between elites and the general population. As economic growth has not been widely shared, large portions of the public believe capitalism is failing to deliver basic affordability, much less upward mobility.

The new trust destroyers are social media and artificial intelligence, which create lots of advantages in terms of productivity and reach, but which are often used to create deception and fraud as well. Experts see technological change, especially generative AI, having accelerated social fragmentation.

Columbia law professor Tim Wu uses the term “extraction economy” to describe the business model in which tech companies grow powerful, not by selling products directly, but by continuously harvesting something from users – primarily attention, behavior, and personal data. Platforms design algorithms to keep people engaged for as long as possible. Every click, search, or swipe becomes information that can be analyzed, predicted, and ultimately sold to advertisers or used to shape future behavior. The result is not only a concentration of economic and cultural power in a handful of companies, but a relationship devoid of trust.

How To Build Trust in a World of Distrust

If we are serious about building a better future, restoring trust is not peripheral work. It is foundational.

Trust does not drift upward on its own. It must be cultivated deliberately—one clarified expectation, one kept commitment, one repaired mistake at a time. Built patiently, it remains the most renewable resource leadership possesses, and we can start at any time to build trust in a world where nobody trusts anybody anymore.

Robert Putnam demonstrated decades ago that civic engagement and cooperation reinforce one another. Small acts—honoring a deadline, giving credit generously, admitting uncertainty—ripple outward. In organizations navigating technological upheaval, these micro-behaviors create emotional stability that strategy alone cannot supply.

Perhaps the best-known trust guru is Stephen M. R. Covey, who argues that trust is not merely a moral virtue; it is a learnable competency. Covey, the son of famed “Seven Habits” author Stephen Covey, teaches that trust grows from consistent behavior, not charisma or intention. Leaders often harbor the mistaken idea that trust is something bestowed upon them because of position or expertise. Instead, argues Covey, it accumulates through observable habits repeated over time. Covey emphasizes credibility—the alignment of character and competence. Character asks whether you are honest and motivated by shared benefit. Competence asks whether you can deliver results.

Charles Feltman, author of The Thin Book of Trust: An Essential Primer for Building Trust at Work, approaches trust from a unique angle. His definition of trust is relational: “choosing to risk making something you value vulnerable to another person’s actions.” Feltman identifies four assessments people make when deciding whether to trust someone: sincerity, reliability, competence, and care. Most breakdowns occur, says Feltman, not because of dramatic betrayal, but because expectations were never clarified.

In practical terms, this means leaders must become unusually precise communicators. Reliability is strengthened when commitments are explicit and modest rather than vague and ambitious. A manager who promises weekly updates and delivers them faithfully builds more trust than one who announces sweeping transformation but repeatedly misses deadlines. In accelerated environments where plans quickly become obsolete, Feltman encourages renegotiating commitments openly. Silence erodes trust faster than bad news.

Both Covey and Feltman emphasize the power of repair. Distrust grows when mistakes are hidden or minimized. Trust grows when harm is acknowledged quickly and concretely. In organizations facing AI disruption or restructuring, leaders who communicate early and empathetically often preserve loyalty even through painful transitions. People are more willing to endure change when they believe they are being treated honestly.

For leaders, building and maintaining trust is not an abstract academic conversation. In a world shaped by exponential technologies and volatile narratives, trust is a performance advantage. High trust reduces friction and speeds execution. Low trust multiplies oversight, legal review, defensive communication, and second-guessing.

In the Age of Acceleration, building trust truly matters.

This article originally appeared in Forbes

Image credit: Pixabay

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5 Ways to Protect Your Career from AI Job Displacement

How To Protect Your Career From AI Job Displacement

GUEST POST from Robert B. Tucker

I don’t want to sound like an alarmist, but if your work involves sitting at a computer, your job could be in jeopardy. The pace of progress in AI has become exponential rather than linear, as AI models are becoming capable of building AI models. As the implications of recent advances cascade throughout the economy, stock markets gyrate, and career anxiety pervades the white-collar sector.

As a futurist and innovation expert advising organizations for over three decades, I have had a front-row seat to many varieties of disruptions. This experience has led me to conclude that technological innovations rarely eliminate those who are willing to experiment and adapt. Most at risk are those who are complacent: those who assume they can get by without fundamentally changing how they operate.

“Something big is happening,” noted AI investor and CEO Matt Shumer, in an influential post read by 80 million people. “I am no longer needed for the actual technical work of my job. I describe what I want to be built, in plain English, and it just appears. Not a rough draft I need to fix. The finished thing. I tell the AI what I want, walk away from my computer for four hours, and come back to find the work done better than I would have done it myself.”

The first big warning of mass job displacement came in 2025, when Anthropic CEO Dario Amodei warned in an Axios interview that AI could eliminate “roughly 50% of entry-level white-collar jobs within 1–5 years, and that unemployment could spike to 10–20% within one to five years. “

Following Matt Shumer’s post last week, Citrini Research earlier this week tapped into a new strain of fears about AI, painting what the Wall Street Journal called a “dark portrait of a future in which technological change inspires a race to the bottom in white-collar knowledge work. “For the entirety of modern economic history, human intelligence has been the scarce input,” Citrini noted. “We are now experiencing the unwind of that premium.” The Dow dropped 820 points on the post.

The question on everyone’s mind right now seems to be: What happens when artificial intelligence can do my job faster, cheaper, and perhaps better than I can? But as a futurist and innovation consultant, I believe there’s a better question that one can ask: In what ways do I protect my career when the pace of AI progress is exponential, rather than linear?

My suggestions are below:

1. Stop Trying to Compete with AI on Efficiency. Compete on value

If your primary value add comes from sitting at a computer processing information, summarizing documents, generating reports, or performing predictable analysis, AI systems are intent on making you redundant. My suggestion here is to alter your value proposition.

In the legal arena, AI can conduct research, analyze and draft contracts, and otherwise do the job of entry-level workers. In healthcare, AI can read scans, analyze lab results, review medical journals, and suggest diagnoses. In customer service, genuinely capable AI agents are often more competent than call center workers. In 2023, AI struggled to write code. Today, at a growing number of companies, AI is writing much of the code.

Three years ago, AI could generate text but struggled to reason. In 2026, it solves complex problems step-by-step. In 2022, AI needed constant prompting. Today, agentic systems are planning and executing multi-stage projects on their own. And where AI once missed human nuance entirely, it is beginning to recognize emotion and adapt responses accordingly. You get the idea; AI is assaulting assumptions about what it can and cannot do at every juncture.

Many professionals unknowingly position themselves as competitors to automation. But competing on efficiency or productivity alone is a losing battle. To shift, ask yourself a different question: What do I uniquely contribute when the data is already available?

2. Become AI-fluent, starting today

NVIDIA CEO Jensen Huang warned in May 2025 at the Milken Institute Global Conference, “You’re not going to lose your job to an AI, you’re going to lose it to someone who uses AI.” Why not be that person instead?

In Build a Better Future: 7 Mindsets for Navigating the Age of Acceleration, I describe the Preparedness Mindset as most important of all — proactively anticipating change rather than reacting too late. Preparedness demands that, regardless of any misgivings about AI, we lean in to it, we become experts in it, and we design effective early warning systems to keep us abreast.

My suggestion is: spend time each week using new AI tools to draft communications, analyze data, brainstorm strategy, simulate customer conversations, and stress-test ideas. In doing so, you are not just learning to use new software. You are learning collaboration with a new type of intelligence. Those who understand what AI can and cannot do become indispensable translators between technology and business results. There’s no time to waste in becoming AI-fluent.

3. Hone your innovation skills

When the personal computer arrived, some employees feared it. Others stayed late learning spreadsheets and word processing. Within a few years, the difference in career trajectory was unmistakable. This same dynamic is unfolding again.

Tens of thousands of white-collar jobs are vanishing as AI starts to bite. Yet today organizations are desperately in need of people with an opportunity mindset – the outward focus to “find a (customer) need and fill it,” and to get new projects done, improve customer experience, motivate teams, enter new markets, and achieve unconventional results.

Human agency — the willingness to initiate action rather than await instruction — becomes a career differentiator. That might mean: proposing new AI-enabled services to clients, redesigning workflows, volunteering for experimental projects, or building personal expertise outside formal job descriptions. History shows that disruption rewards proactive learners who act on their ideas.

4. Move Closer to Problems, Not Tasks

AI replaces tasks faster than it replaces responsibility. Professionals who define themselves narrowly — “I prepare quarterly reports” or “I write marketing copy” — face greater exposure than those who own outcomes.

Executives increasingly value people who solve problems rather than execute assignments.

Consider shifting your identity toward improving customer retention, accelerating product innovation, strengthening culture, managing risk, or enabling growth. Tasks may change as AI evolves. Problems remain. This reflects what I call the Adaptability and Human Agency Mindsets — expanding your role faster than disruption can shrink it.

5. Develop A Long View of Value Creation

Periods of technological upheaval tempt people toward short-term survival thinking. Yet careers are marathons measured over decades. The professionals who flourish are those who continually reinvent how they add value.

Three forward-looking questions:

  • What skills will matter more five years from now?
  • What emerging problems will organizations struggle to solve?
  • Where can I become known as a trusted guide?

The Long View mindset encourages investing in capabilities that compound over time: leadership presence, interdisciplinary thinking, ethical judgment, and strategic foresight. Ironically, these human-centered abilities become more valuable as machines grow more capable.

The Opportunity Hidden Inside the Fear

As the futurist Thomas Koulopoulos observed in Gigatrends: Six Forces That Are Changing the Future for Billions, “As a species, we consistently allow the peril of the present to eclipse the promise of the future, and by doing that, we fail to comprehend just how much we can accomplish.”

Artificial intelligence will undoubtedly reshape entry-level work and certain knowledge professions. But history suggests something equally important: entirely new roles emerge alongside disruption. Entirely new opportunities will inevitably arise as well.

The printing press eliminated scribes but created publishers. The internet disrupted travel agents, yet produced digital marketing, cybersecurity, and platform entrepreneurship. AI will do the same.

The essential question is not whether change is coming. It is whether we as individuals choose to become passengers or navigators.

In an accelerated age, the safest career strategy is not hiding from technology but running toward it — with curiosity, agency, and vision. Those who learn fastest, adapt deliberately, and commit themselves to solving meaningful problems will not merely avoid displacement. They will help build the future that others are still struggling to understand.

This article originally appeared in Forbes

Image credit: Unsplash

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